SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 2811–2820 of 3304 papers

TitleStatusHype
Automatic dimensionality reduction of Twin-in-the-Loop Observers—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
Automatic Prediction of the Performance of Every Parser—0
Automatic Selection of t-SNE Perplexity—0
Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response—0
Autonomous Dimension Reduction by Flattening Deformation of Data Manifold under an Intrinsic Deforming Field—0
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images—0
Auto-weighted Mutli-view Sparse Reconstructive Embedding—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified